Automating Crop Canopy Data Collection for Crop Management

Contact: Michael Gifford Michael.Gifford@niab.com

Models are used to optimise the production of potato crops by forecasting yield and scheduling irrigation. The models require regular data on the percentage of ground covered by leaves to quantify light interception and evapotranspiration. Currently this is collected by users on the ground but this is time-consuming, expensive and often poorly performed. Automating the collection of data would improve the accuracy of model outputs and enable wider use by reducing the cost of operation. Satellites with optical sensors can collect suitable data, but in Northern Europe, cloud cover prevents reliably collecting observations on the required weekly basis. The EU’s Copernicus Programme provides free high resolution optical imagery and synthetic aperture radar (SAR) imagery from the Sentinel 1 and 2 satellites. SAR imagery can be collected through clouds and during the night. At present there is no available service for estimating canopy cover from SAR imagery. The challenge for the teams will be to develop a system for estimating canopy cover from SAR imagery and to integrate this into the existing digital model for Potato Yield Forecasting. NIAB has extensive existing data sets of “ground truthed” canopy cover at defined locations which will be provided to the team. We anticipate that the project will likely require expertise in machine learning, image analysis and manipulation techniques database development and the use of APIs.